Diffusion model for galaxy generation
Project description
Contains 4 generative diffusion models ScoreNet32 and ScoreNet64 for both the HSC and ZTF surveys. These are used to return the gradients of an arbitrary image with respect to a prior distribution of individual artifact free galaxy models. Current functions include ScoreNetXX(image) returns gradients as stated. Data transformatons are now done inside the package.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
galaxygrad-0.1.0.tar.gz
(24.7 MB
view details)
Built Distribution
File details
Details for the file galaxygrad-0.1.0.tar.gz
.
File metadata
- Download URL: galaxygrad-0.1.0.tar.gz
- Upload date:
- Size: 24.7 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.13
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 037b2045864b5123e75b6eb42ef37454b3d6bc1d13c8ca14407f27b8d4ca5791 |
|
MD5 | b256111bcbab838f1be0a7355911d4ec |
|
BLAKE2b-256 | 77f015b75d0c243df1fcfc266e317106819f64e2fe40ab9e45ac925ba104f9d4 |
File details
Details for the file galaxygrad-0.1.0-py3-none-any.whl
.
File metadata
- Download URL: galaxygrad-0.1.0-py3-none-any.whl
- Upload date:
- Size: 24.7 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.13
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 794cd4b22eb27547c3f902d74a193ab46d6e08b3f0923ad379543df26431eb5c |
|
MD5 | 8ae95cd0c51f43c202f892b827701ff8 |
|
BLAKE2b-256 | 4f9ce3ab109b18eef3b72e15da4bfc19f24c473b26bbe2f005cdd370c6ab5a8b |